[wip] Add DeepseekV3 SFT config

apps/sft/deepseek_v3.yaml

@@ -0,0 +1,70 @@
+# >>> python -m apps.sft.main --config apps/sft/deepseek_v3.yaml
+
+
+# TODO: required by torchtitan
+# https://github.com/pytorch/torchtitan/blob/2f1c814da071cc8ad165d00be6f9c1a66f8e1cce/torchtitan/distributed/utils.py#L265
+comm:
+  trace_buf_size: 0
+
+model_name: "deepseek-ai/DeepSeek-V3.1-Base"
+
+model:
+  name: deepseek_v3
+  flavor: 16B
+  hf_assets_path: hf://${model_name}
+
+processes:
+  procs: 8
+  with_gpus: true
+
+optimizer:
+  name: AdamW
+  lr: 1e-5
+  eps: 1e-8
+
+lr_scheduler:
+  warmup_steps: 200
+
+training:
+  local_batch_size: 1
+  seq_len: 2048
+  max_norm: 1.0
+  steps: 1000
+  compile: false
+  dataset: "c4"
+
+parallelism:
+  data_parallel_replicate_degree: 1
+  data_parallel_shard_degree: -1
+  tensor_parallel_degree: 1
+  pipeline_parallel_degree: 1
+  context_parallel_degree: 1
+  expert_parallel_degree: 1
+  disable_loss_parallel: false
+
+checkpoint:
+  enable: true
+  folder: ./checkpoint              # The folder to save checkpoints to.
+  initial_load_path: hf://${model_name}  # The path to load the initial checkpoint from. Ignored if `folder` exists.
+  initial_load_in_hf: true          # If true, interpret initial_load_path as a HuggingFace model repo
+  last_save_in_hf: true
+  interval: 500
+  async_mode: "disabled"
+
+activation_checkpoint:
+  mode: selective
+  selective_ac_option: op
+
+metric_logging:
+  wandb:
+    project: sft-training
+    group: sft_exp_${oc.env:USER}
+    logging_mode: global_reduce # global_reduce, per_rank_reduce, per_rank_no_reduce
+
+# profiling:
+#   enable_profiling: false
+
+# metrics:
+#   log_freq: 10
+#   enable_tensorboard: true
+#   save_tb_folder: "tb"